2026-05-26T10:15:17Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:1721 2023-04-05T13:24:34Z openaire_data community-mcarchive

Lopanitsyna, Nataliya Ceriotti, Michele Lopanitsyna, Nataliya Fraux, Guillaume Springer, Maximilian A. De, Sandip Ceriotti, Michele 2023-04-05 Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set. In this record, we provide a dataset containing 25,000 structures utilized for fitting the aforementioned potential, with a focus on 25 d-block transition metals, excluding Tc, Cd, Re, Os and Hg. text/markdown application/gzip application/octet-stream application/gzip text/markdown https://doi.org/10.24435/materialscloud:73-yn oai:materialscloud.org:1721 mcid:2023.57 eng Materials Cloud https://doi.org/10.48550/arXiv.2212.13254 https://chemiscope.materialscloud.io/?load=https%3A%2F%2F127.0.0.1%2Fapi%2Frecords%2Fjkaf5-anv04%2Ffiles%2FHEA25.chemiscope.json.gz%2Fcontent%3Ffilename%3DHEA25.chemiscope.json.gz%26materials_cloud_doi%3D10.24435%2Fmaterialscloud%3A73-yn https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:c2-zs info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode machine learning high-entropy alloys neural network potential alchemical compression MARVEL Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25 info:eu-repo/semantics/other